Patients With Head and Neck Cancer and High Health Care Costs
Bibliographic record
Abstract
Importance: The care for a small subset of patients is responsible for a disproportionately large share of health care expenditures. Head and neck cancer is associated with significant health care costs due to complex treatment regimens and long-term sequelae. Given this high baseline cost, identifying patients with high care costs within a population with cancer might help inform interventions to optimize resource allocation. Objective: To characterize patients with head and neck cancer with the highest health care costs during the first year after diagnosis. Design, Setting, and Participants: A population-based, retrospective cohort study was conducted using administrative data from the Institute for Clinical and Evaluative Sciences in Ontario, Canada, and included adults diagnosed with head and neck cancer between January 2007 and October 2020 (identified from the provincial cancer registry) with a full 1.5-year follow-up from the date of diagnosis to the date of death or October 31, 2021. The total 1-year health care costs were estimated using a patient-level algorithm and were collected in 2020 Canadian dollar values. The main analyses were performed in April 2023 and a sensitivity analysis was performed in April 2025. Main Outcomes and Measures: High health care costs (>75th percentile) during the first year after a head and neck cancer diagnosis. Predictors of high health care costs were identified using a multivariable logistic regression model. Results: The cohort included 13 795 patients (mean age, 63.2 [SD, 11.7] years and 3452 [25.0%] were female), 3448 (25%) of whom had high health care costs. Cancer stage was the strongest predictor of high health care costs. Compared with patients with stage I cancer, those with stage II cancer had 2-fold greater odds for high health care costs (odds ratio [OR], 3.14 [95% CI, 2.56-3.84]), those with stage III cancer had 5-fold greater odds for high health care costs (OR, 6.08 [95% CI, 4.99-7.41]), and those with stage IV cancer had 8-fold greater odds for high health care costs (OR, 8.94 [95% CI, 7.43-10.80]). Receiving multiple treatment modalities also was associated with greater odds for high-cost care. Conclusions and Relevance: This cohort study found that more advanced disease stage and receiving multiple treatment modalities were the strongest predictors of high-cost care among patients diagnosed with head and neck cancer. Prioritizing research and implementation of screening programs, earlier cancer diagnoses, and effective treatment deescalation strategies might mitigate a significant portion of these high costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".